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学生对 IBM 提供的 Machine Learning with Apache Spark 的评价和反馈

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课程概述

Explore the exciting world of machine learning with this IBM course. Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos. Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML. In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course....

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AS

Feb 2, 2024

Excellent learning experience - well-designed content and practical sessions.

SS

Mar 22, 2024

Spark is build to work with distributed systems. But here it looks like it is some strange twin of pandas. Some basics of Distributed Systems wanted with at least one at least theoretical examlple.

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26 - Machine Learning with Apache Spark 的 26 个评论（共 26 个）

创建者 Tom W

•

Sep 27, 2024

Pretty good. I know feel moderately confident in using the spark shell. I'd have liked some more details about precisely how the partitioning of work for ML functions worked. I was a bit annoyed that the final project was more or less identical to the final project, which was a bit tedious. In general the exercises were kind of tedious are formulaic, but by the end it started being a bit fun because you came to remember all the times. It was good that this gave you hands on experience with pyspark.